Papers with Code 아카이브

Multi-Task Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 2
평가표: 8
보존된 평가 기록: 16

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

<span style="color:grey; opacity: 0.6">( Image credit: Cross-stitch Networks for Multi-task Learning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/thudm/glm-130b

공식 코드

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thuiar/Self-MM

공식 코드

Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis

https://github.com/tianheyu927/PCGrad

공식 코드

Gradient Surgery for Multi-Task Learning

https://github.com/tiankaihang/min-snr-diffusion-training

공식 코드

Efficient Diffusion Training via Min-SNR Weighting Strategy

https://github.com/tiemink/MTL_TaskTensioner

공식 코드

Leveraging convergence behavior to balance conflicting tasks in multi-task learning

https://github.com/tiianhk/label-metric

공식 코드

Hybrid Losses for Hierarchical Embedding Learning

https://github.com/timbervision/timbervision

공식 코드

TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations

https://github.com/timmy-chan/miss

공식 코드

MISS: A Generative Pretraining and Finetuning Approach for Med-VQA

https://github.com/tingruew/TTITA-Text-Imputation

공식 코드

Table Transformers for Imputing Textual Attributes

https://github.com/tissueimageanalytics/cerberus

공식 코드

One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification

https://github.com/tkgsn/priv_traj_gen

공식 코드

HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization

https://github.com/tmlr-group/fabflex

공식 코드

Fast and Accurate Blind Flexible Docking

https://github.com/tmtvaa/abaw4

공식 코드

An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning

https://github.com/tobwei/aptai

공식 코드

Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech

https://github.com/tonyby/m3

공식 코드

M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval

https://github.com/tonyby/ramqa

공식 코드

RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering

https://github.com/torchjd/torchjd

공식 코드

Jacobian Descent for Multi-Objective Optimization

https://github.com/tpark94/spnv2

공식 코드

Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap

https://github.com/transbiozi/mtlcomb

공식 코드

MTLComb: multi-task learning combining regression and classification tasks for joint feature selection

https://github.com/transducens/mtl-da-emnlp

공식 코드

Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach

https://github.com/tristandeleu/synergies-disentanglement-sparsity

공식 코드

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

https://github.com/tshi04/DMSC_FEDA

공식 코드

An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis

https://github.com/tsinghua-fib-lab/recsys2023-sine

공식 코드

Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation

https://github.com/tsinghua-msiip/uni-med

공식 코드

Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE

https://github.com/tstandley/taskgrouping

공식 코드

Which Tasks Should Be Learned Together in Multi-task Learning?

https://github.com/ttslr/strengthnet

공식 코드

StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis

https://github.com/tu-rbo/concarne

공식 코드

Patterns for Learning with Side Information

https://github.com/tunazislam/covid_fb_ad_minimalsup

공식 코드

Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision

https://github.com/tushar117/Transformer-Models-for-Text-Coherence-Assessment

공식 코드

Transformer Models for Text Coherence Assessment

https://github.com/uber/ludwig

공식 코드

Ludwig: a type-based declarative deep learning toolbox

https://github.com/ucl-candi/freehand

공식 코드

Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames

https://github.com/uds-lsv/semantically_enriched_awes

공식 코드

Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings

https://github.com/uiuctml/TaskVectorBasis

공식 코드

Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable Approach

https://github.com/uiuctml/localize-and-stitch

공식 코드

Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic

https://github.com/ukplab/emnlp2020-multicqa

공식 코드

MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale

https://github.com/ukplab/iclr2024-model-merging

공식 코드

Model Merging by Uncertainty-Based Gradient Matching

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.